Browse Articles

Climate-denying rumor propagation in a coupled socio-climate model: Impact on average global temperature

Individual attitudes vastly affect the transformations we are experiencing and are vital in mitigating or intensifying climate change. A socio-climate model by coupling a model of rumor dynamics in heterogeneous networks to a simple Earth System model is developed, in order to analyze how rumors about climate change impact individuals’ opinions when they may choose to either believe or reject the rumors they come across over time. Our model assumes that when individuals experience an increase in the global temperature, they tend to not believe the rumors they come across. The rumor rejectors limit their CO2 emissions to reduce global temperature. Our numerical analysis indicates that, over time, the temperature anomaly becomes less affected by the variations in rumor propagation parameters, and having larger groups (having more members) is more efficient in reducing temperature (by efficiently propagating rumors) than having numerous small groups. It is observed that decreasing the number of individual connections does not reduce the size of the rejector population when there are large numbers of messages sent through groups. Mitigation strategies considered by the rejectors are highly influential. The absence of mitigative behavior in rejectors can cause an increase in the global average temperature by 0.5°C. Our model indicates that rumor propagation in groups has the upper hand in controlling temperature change, compared to individual climate-denying propagation.

A simulation study to quantitatively assess the performance of electronic prescribing systems in English NHS Hospital Trusts

Volatility, correlation and risk spillover effect between freight rates in BCI and BPI markets: Evidence from static and dynamic GARCH-Copula and dynamic CoVaR models

The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.

Horticultural activity in soil inoculated with Streptomyces rimosus improved depressive mood with altered electroencephalogram and serum metabolism in adults

Heritable polygenic editing: the next frontier in genomic medicine?

Abstract Polygenic genome editing in human embryos and germ cells is predicted to become feasible in the next three decades. Several recent books and academic papers have outlined the ethical concerns raised by germline genome editing and the opportunities that it may present1–3. To date, no attempts have been made to predict the consequences of altering specific variants associated with polygenic diseases. In this Analysis, we show that polygenic genome editing could theoretically yield extreme reductions in disease susceptibility. For example, editing a relatively small number of genomic variants could make a substantial difference to an individual’s risk of developing coronary artery disease, Alzheimer’s disease, major depressive disorder, diabetes and schizophrenia. Similarly, large changes in risk factors, such as low-density lipoprotein cholesterol and blood pressure, could, in theory, be achieved by polygenic editing. Although heritable polygenic editing (HPE) is still speculative, we completed calculations to discuss the underlying ethical issues. Our modelling demonstrates how the putatively positive consequences of gene editing at an individual level may deepen health inequalities. Further, as single or multiple gene variants can increase the risk of some diseases while decreasing that of others, HPE raises ethical challenges related to pleiotropy and genetic diversity. We conclude by arguing for a collectivist perspective on the ethical issues raised by HPE, which accounts for its effects on individuals, their families, communities and society4.

Correction: Diversity of lithophytic moss species in karst regions in response to elevation gradients

FRET analysis of the unwrapping of nucleosomal DNA containing a sequence characteristic of the + 1 nucleosome

Trends of adverse pregnancy outcomes and its determinants in Arba Minch Zuria and Gacho Baba Woredas from 2018 to 2022: Analysis of health and demographic surveillance data

Introduction Many family members and the expectant mother view pregnancy as a time of joyful anticipation. However, it can also bring about a range of issues that may pose serious and potentially life-threatening risks for both the mother and the unborn child. Adverse pregnancy outcomes are an alarming public issue in different parts of the world and have a seriously harmful influence on both their health and well-being. Nevertheless, less is known about trends and determinates of adverse pregnancy outcomes in the Arba Minch zuria and Gacho Baba districts in southern Ethiopia. Therefore, this study aimed to fill these gaps in the study setting. Methods An open, dynamic cohort study design was employed among 8885 pregnancies from 2018 to 2022, health and demographic surveillance data were involved. Data to be collected at Arba Minch Health and Demographic Surveillance Site (HDSS) using a structured questionnaire. A log-binomial regression model was used to identify determinates. A P-value of less than 0.05 was considered to declare a statistically significant association. Results In this study, the overall adverse pregnancy outcomes were 8.89 per 1000 live births [95%CI: 6.93, 10.84]. The stillbirth and abortion rates were 5.74 per 1000 live births (95%CI: 4.36, 7.54) and 3.15 per 1000 live births [95%CI: 1.97, 4.02], respectively. The trends of adverse pregnancy outcomes showed 11.1/1000 in 2018 and 14.1/1000 in 2022. Age > 34 years old (aPPR = 2.93, 95%CI: 1.67, 5.17), antenatal care (aPPR = 0.52, 95%CI: 0.33, 0.83), and history of pregnancy loss (aPPR = 2.68, 95%CI: 1.36, 5.29) were identified as determinates for adverse pregnancy outcomes. Conclusion The prevalence of adverse pregnancy outcomes is still high, and trends vary from time to time. As such, attention is needed for the women who had a previous history of pregnancy loss, and addressing those determinants could potentially reduce the rates of stillbirths and abortions, ultimately promoting healthier pregnancies and better pregnancy outcomes.

Fuzzy self-tuning fractional order PD permanent magnet synchronous motor speed control based on torque compensation

Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological sector or cryptocurrencies. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management (PM) is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator, also called gymnasium. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, capable of representing this distribution over time, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, to assess whether they follow a reasonable behaviour, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, driven by the motivation of making DRL explainable, we developed a novel Explainable DRL (XDRL) approach for PM, integrating the Proximal Policy Optimization (PPO) DRL algorithm with the model agnostic explainable machine learning techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent’s suggestions. We empirically illustrate it by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time. We propose the first explainable post hoc PM financial policy of a DRL agent.

Three-dimensional linkage analysis with digital PCR for genome integrity and identity of recombinant adeno-associated virus

Research on reachable set boundary of neutral system with various types of disturbances

This study delves into neutral-type systems (NTSs), emphasizing the critical role of defining precise reachable set (RS) boundaries for safe and efficient system design and operation. The investigation notably addresses the challenges posed by time-varying delays, applying Lyapunov’s direct method alongside advanced matrix inequality techniques to identify minimized and more accurate ellipsoidal boundaries of the RS in NTSs influenced by bounded and nonlinear disturbances. Our findings, verified through numerical simulations and comparisons with existing literature, demonstrate enhanced control and management capabilities for complex systems, thus underscoring the substantial theoretical and practical value of incorporating delay elements in NTSs.

Iron metabolism in a mouse model of hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) remains the most prevalent type of primary liver cancer worldwide. p53 is one of the most frequently mutated tumor-suppressor genes in HCC and its deficiency in hepatocytes triggers tumor formation in mice. To investigate iron metabolism during liver carcinogenesis, we employed a model of chronic carbon tetrachloride injections in liver-specific p53-deficient mice to induce liver fibrosis, cirrhosis and subsequent carcinogenesis. A transcriptome analysis of liver carcinoma was employed to identify p53-dependent gene expression signatures with subsequent in-depth analysis of iron metabolic parameters being conducted locally within liver cancers and at systemic levels. We show that all mutant mice developed liver cancer by 36-weeks of age in contrast to 3.4% tumors identified in control mice. All liver cancers with a p53-deficient background exhibited a local iron-poor phenotype with a “high transferrin receptor 1 (Tfr1) and low hepcidin (Hamp)” signature. At systemic levels, iron deficiency was restricted to female mice. Additionally, liver tumorigenesis correlated with selective deficits of selenium, zinc and manganese. Our data show that iron deficiency is a prevalent phenomenon in p53-deficient liver cancers, which is associated with alterations in Hamp and Tfr1 and a poor prognosis in mice and patients.

Spatial-temporal dynamics and driving factors of urban construction land in mega cross-river mountain cities: A case study of Chongqing’s central urban area, China

The evolutionary model of construction land serves as a fundamental pillar in national spatial development and planning research. However, previous studies have overlooked the "climbing" mode of construction land on three-dimensional terrains. To address this issue, utilizing elevation data and land use data from 2010 to 2020, this study employs slope analysis, intensity analysis, spatio-temporal transformation, and PLUS model to elucidate the spatial expansion process and driving forces of urban construction land in Chongqing from both two-dimensional and three-dimensional perspectives. The findings indicate that: (1) From a three-dimensional topographical standpoint, between 2010 and 2012, construction land gradually expanded towards low-slope areas, whereas between 2012 and 2020, it progressively extended into high-slope regions. (2) Regarding land type conversion patterns, the shift from arable land to construction land demonstrates a systematic inclination, while other transformations exhibit absolute or relative tendencies. Conversely, the conversion from construction land to arable land also displays a systematic pattern. (3) Since 2010, the growth process of construction land has transitioned from slow-equilibrium to rapid-disequilibrium with an expanding spatial disparity. (4) Most areas maintain relatively stable spatial conditions without significant jumps or transitions observed. (5) The expansion of construction land in Chongqing is primarily influenced by terrain, river, tunnel, rail transit, and other factors. The outcomes of this study can provide scientific foundations and decision-making references for rational planning in similar cities characterized by mountainous landscapes intersected by rivers.

Optimizing cryptographic protocols against side channel attacks using WGAN-GP and genetic algorithms

Longitudinal host transcriptional responses to SARS-CoV-2 infection in adults with extremely high viral load

Current understanding of viral dynamics of SARS-CoV-2 and host responses driving the pathogenic mechanisms in COVID-19 is rapidly evolving. Here, we conducted a longitudinal study to investigate gene expression patterns during acute SARS-CoV-2 illness. Cases included SARS-CoV-2 infected individuals with extremely high viral loads early in their illness, individuals having low SARS-CoV-2 viral loads early in their infection, and individuals testing negative for SARS-CoV-2. We could identify widespread transcriptional host responses to SARS-CoV-2 infection that were initially most strongly manifested in patients with extremely high initial viral loads, then attenuating within the patient over time as viral loads decreased. Genes correlated with SARS-CoV-2 viral load over time were similarly differentially expressed across independent datasets of SARS-CoV-2 infected lung and upper airway cells, from both in vitro systems and patient samples. We also generated expression data on the human nose organoid model during SARS-CoV-2 infection. The human nose organoid-generated host transcriptional response captured many aspects of responses observed in the above patient samples, while suggesting the existence of distinct host responses to SARS-CoV-2 depending on the cellular context, involving both epithelial and cellular immune responses. Our findings provide a catalog of SARS-CoV-2 host response genes changing over time and magnitude of these host responses were significantly correlated to viral load.

Heavy metals impact environmental capacity of oasis soils in Qinghai-Tibet Plateau dry zone

Optimization of surface roughness for titanium alloy based on multi-strategy fusion snake algorithm

Titanium alloy is known for its low thermal conductivity, small elastic modulus, and propensity for work hardening, posing challenges in predicting surface quality post high-speed milling. Since surface quality significantly influences wear resistance, fatigue strength, and corrosion resistance of parts, optimizing milling parameters becomes crucial for enhancing service performance. This paper proposes a milling parameter optimization method utilizing the snake algorithm with multi-strategy fusion to improve surface quality. The optimization objective is surface roughness. Initially, a prediction model for titanium alloy milling surface roughness is established using the response surface method to ensure continuous prediction. Subsequently, the snake algorithm with multi-strategy fusion is introduced. Population initialization employs an orthogonal matrix strategy, enhancing population diversity and distribution. A dynamic adaptive mechanism replaces the original static mechanism for optimizing food quantity and temperature, accelerating convergence. Joint reverse strategy aids in selecting and generating individuals with higher fitness, fortifying the algorithm against local optima. Experimental results across five benchmarks employing various optimization algorithms demonstrate the superiority of the MSSO algorithm in convergence speed and accuracy. Finally, the multi-strategy snake algorithm optimizes the objective equation, with milling parameter experiments revealing a 55.7 percent increase in surface roughness of Ti64 compared to pre-optimization levels. This highlights the effectiveness of the proposed method in enhancing surface quality.

Identification of a metabolic brain network characterizing essential tremor

Abstract The neuronal correlate of tremor genesis and cognitive function in essential tremor (ET) and its modulation by deep brain stimulation (DBS) are poorly understood. To explore the underlying metabolic topography of motor and cognitive symptoms, sixteen ET patients (age 63.6 ± 49.1 years) and 18 healthy controls (HC) (61.1 ± 6.3 years) underwent tremor and cognitive assessments and18F-fluorodeoxyglucose PET of the brain. Multivariate spatial covariance analysis was applied for identifying ET related metabolic brain networks. For network validation and to explore DBS effects, 8 additional ET patients (68.1 ± 8.2 years) treated with DBS were assessed in both the ON and OFF state, respectively. The ET related metabolic spatial covariance pattern (ETRP) was characterized by relatively increased metabolism in the cerebellum, brainstem, and temporo-occipital cortices, accompanied by relative metabolic decreases mainly in fronto-temporal and motor cortices. Network expression showed inverse correlations with tremor severity and disease duration and positive correlations with cognitive dysfunction. DBS substantially alleviated tremor, but had only marginal effects on cognitive performance. There were no significant DBS effects on ETRP expression at the group level, but all but one subject showed higher scores in the ON state. Our findings suggest ET is characterized by an abnormal brain network associated with disease phenotype.

Discovery of a potential open ocean nursery for the endangered shortfin mako shark in a global fishing hotspot

Populations of large pelagic sharks are declining worldwide due to overfishing. Determining the overlap between shark populations and fishing activities is important to inform conservation measures. However, for many threatened sharks the whereabouts of particularly vulnerable life-history stages – such as pregnant females and juveniles – are poorly known. Here, we investigated the spatial distribution of size classes, energy transfer and reproductive states of pregnant females of the endangered shortfin mako, Isurus oxyrinchus, using spatially resolved catch data from a Spanish surface longline vessel (1996 − 2009) in the South-east Pacific Ocean. Our results suggest a general eastward gradient of occurrence of pregnant females of thousands of kilometers from western oceanic feeding grounds towards the eastern Pacific, where we observed an aggregation area of small juveniles. Moreover, the potential nursery likely overlapped a longline fishing hotspot, increasing the vulnerability of juveniles from fisheries. Our results suggest that limiting fishing pressure in this area could reduce mortality of early life stages and contribute to the conservation of this endangered shark species.